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10 Challenges That Data Science Industry Still Faces


10 Challenges That Data Science Industry Still Faces


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Most industries today are resorting to data and analytics — for accomplishing tasks that were earlier thought impossible given the size, disparity and uneven distribution. It has become a crucial part of the overall working of most companies.

While the adoption of analytics has increased, it comes with its own set of challenges. Keeping up and close with the analytics heads of various companies, it throws light on how the companies need to buckle up and solve the challenges that come with analytics adoption — whether it is finding the right talent or solving primary challenges revolving around getting the raw material organised, hidden security vulnerabilities and more.



In this article, we list down 10 such challenges that the data science industry still faces despite the spectacular growth that has been witnessed with its adoption over the years.

These insights are gained by inputs from our previous interviews.

1| Hiring New Talent With Required Skills:

This tops the list of challenges as most companies are grappling with the acute talent shortage. Many analytics heads believe that the actual challenge that Indian analytics industry faces is the dearth of skills within the workforce. A significant percentage of Indian professionals are not equipped with the required skill-set catering to evolving business requirement. There is a need for people who can understand and execute complex analytics projects, posing the right balance of analytics skills and business domain knowledge.

Companies today are still struggling to build the right team while ensuring the right infrastructure of hardware and software implementation. There is a lack of talent in the market which has the right mix of business, statistical and programming knowledge.

2| Finding The Right Data & Right Data Sizing:

It goes without saying that the availability of ‘right data’ is the most common problem, and plays a crucial role in building the right model. With the large volume and velocity of data, one of the biggest challenges is to be able to make sense of it all to drive profitable business decisions. Too much data can take the focus away from actionability and lead to data paralysis. It is important to capture data and correct the noise to make a robust analytical model. Data cleaning is necessary for accuracy of models.

Organisations need to ask themselves if they are really equipped to make sense of such large volumes of data and more importantly and whether all the data points are going to be utilised. It is important to understand what is critical and what needs to be measured in order to help with organisational decision making. A lot of time, effort and money are spent on collecting, storing and integrating data sources without first determining how the data will eventually be consumed and by whom.

3| Consolidation of information:

All the industries have overflowing data that is mostly scattered. In such scenarios, consolidation of information remains one of the biggest challenges as most organisations grapple with leveraging internal data systems. The industry is struggling with collecting data into a single purview to reap maximum benefits. It is important to have a unified view of data while enriching the information with analytics-infused data elements.

4| Educating People About What Data Can Do For You:

Despite the importance that analytics and data science technologies have created for themselves, there is still a need to explain the end users about how accumulating and analysing the right data can be useful. Most analytics leaders believe that it is one of the biggest challenges to educate people about what data can do for you. They have to be attuned to asking the right questions so that data can do wonders beyond counting, reporting and aggregating numbers.

It remains one of the major challenges to convince traditional companies to move to a data-driven decision-making process. To overcome this it is important to provide the right use cases highlighting the impact data analytics can have on their business.

5| Stakeholder Commitment & Identifying The Right Area To Invest:

“Apart from the fact that data analytics solutions enable enterprises to pave a path for business process transformations, it also requires a lot of involvement and upfront commitment from domain experts to define future business processes driven by analytics platform” shared Hareesha G of Synechron with AIM. Many service providers do not consider this as a key aspect, but it is important to identify and engage key stakeholders and ensure that the right commitment is obtained from the client side while defining analytics roadmap for them. This is crucial to have a project move in the right direction and deliver the right business impact.

6| Right Storytelling:

Adopting analytics is about dealing with complex and intricate models that could be intimidating for end users to understand. Therefore, it requires a combination of great storytelling skills for data scientists and team members to be able to make the data and the process understandable and to be able to conclude how they can work together to make the best of machine learning models at hand.  

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7| Creating Data Science Models But Not Solving It:

This mostly goes for the data science team that is interested in building the data science models but they might not be necessarily solving business problems. The entire process of adoption of data science solutions to execution can be quite intimidating and it is important to build the models that can solve the challenges in real-time. It requires professionals with a strong problem-solving capability to make this happen and again directs back to the point of recruiting the right talent.

8| Identifying Appropriate Analytics Use Cases:

As the analytics industry is still evolving, many analytics leaders believe that there are not a lot of use cases that actually exist out there. It is a challenge to identify correct data for the appropriate analytics use case. If the right set of data is not identified for a specific use case, there are chances that insights may be incorrect. It may become difficult for them to do a lot of things by themselves such as inspect the content and convince people to adopt it, especially if it is being done for the first time in an organisation. It is a challenge to make its presence felt in the boardroom by establishing itself as the key driving force for major management decisions.

9| Agility:

Usually, the analytics functions are structured in a way that allows little or limited interaction with the end business user. Experts believe that for analytics to be generating more meaningful ways to support a business, it needs to be more agile and in sync with business during the decision-making process.

10| Ensuring The Security Of Data:

Analytics is all about handling a huge volume of data and ensuring the security of data that companies are dealing with remains a big challenge. They need to work on ensuring privacy and making data as safe as possible from any wrong use.



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